Learning Too Much From Too Little: False Face Stereotypes Emerge From a Few Exemplars and Persist via Insufficient Sampling

Face stereotypes are prevalent, consequential, yet oftentimes inaccurate. How do false first impressions arise and persist despite counter-evidence? Building on the overgeneralization hypothesis, we propose a domain-general cognitive mechanism: insufficient statistical learning, or Insta-learn. This mechanism posits that humans are quick statistical learners but insufficient samplers. Humans extract statistical regularities from very few exemplars in their immediate context and prematurely decide to stop sampling, creating and perpetuating locally accurate-but globally inaccurate-impressions. Six experiments (N = 1,565) tested this hypothesis using novel pairs of computer-generated faces and social behaviors by fixing the population-level statistics of face-behavior associations to zero (i.e., no relationship). The initial sample contained either 11, five, or three examples with either a positive, zero, or negative linear relationship between facial features and social behaviors. The sampling procedure contained a free-sampling condition in which participants were free to decide when to stop viewing more examples and a fixed-sampling condition in which participants were forced to view all stimuli before making decisions. Consistent with the Insta-learn mechanism, participants learned novel face stereotypes quickly, with as few as three examples, and did not sample enough when they were given the freedom to do so. This domain-general cognitive mechanism provides one plausible origin of false face stereotypes, demonstrating negative consequences when people learn too much from too little.